Gliomas are the most common tumors of the central nervous system and are classified into grades I-IV based on their histological characteristics. Lower-grade gliomas (LGG) can be divided into grade II diffuse low-grade gliomas and grade III moderate gliomas and have a relatively good prognosis. However, LGG often develops into high-grade glioma within a few years. This study aimed to construct and identify the prognostic value of an inflammatory signature and discover potential drug targets for primary LGG. We first screened differentially expressed genes in primary LGG (TCGA) compared with normal brain tissue (GTEx) that overlapped with inflammation-related genes from MSigDB. After survival analysis, nine genes were selected to construct an inflammatory signature. LGG patients with a high inflammatory signature score had a poor prognosis, and the inflammatory signature was a strong independent prognostic factor in both the training cohort (TCGA) and validation cohort (CGGA). Compared with the low-inflammatory signature group, differentially expressed genes in the high-inflammatory signature group were mainly enriched in immune-related signaling pathways, which is consistent with the distribution of immune cells in the high- and low-inflammatory signature groups. Integrating driver genes, upregulated genes and drug targets data, bromodomain and PHD finger-containing protein 1 (BRPF1) was selected as a potential drug target. Inhibition of BRPF1 function or knockdown of BRPF1 expression attenuated glioma cell proliferation and colony formation.
Pubmed ID: 34926268 RIS Download
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Web application for data storage and analysis to explore brain tumors datasets from Chinese cohorts. Data portal for storage and interactive exploration of multi-dimensional functional genomic data that includes primary and recurrent glioma samples from Chinese cohorts. Allows users to browse DNA mutation profile, mRNA/microRNA expression profile and methylation profile, and to do correlation and survival analysis in specific glioma subtype.
View all literature mentionsWeb tool where one component is front end Xena Browser and another component is back end Xena Hubs. Web based Xena Browser empowers biologists to explore data across multiple Xena Hubs with variety of visualizations and analyses. Xena Hubs host genomics data from laptops, public servers, behind firewall, or in cloud, and can be public or private. Xena Browser receives data simultaneously from multiple Xena Hubs and integrates them into single coherent visualization within browser. Allows users to explore functional genomic data sets for correlations between genomic and/or phenotypic variables.
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